The subject matter disclosed herein relates to machine maintenance techniques for industrial systems. More specifically, the subject matter disclosed herein relates to monitoring and diagnosing the mechanical condition of industrial systems based at least partially upon acoustic analyses.
Certain equipment and facilities, such as power generation equipment and facilities, oil and gas equipment and facilities, manufacturing equipment and facilities, and the like, include a plurality of interrelated systems and processes. For example, power generation plants may include turbine systems and processes for operating and maintaining the turbine systems. Likewise, oil and gas operations may include carbonaceous fuel retrieval systems and processing equipment interconnected via pipelines. During normal operations, the equipment may encounter undesired conditions (e.g., misalignment, looseness, imbalance, etc.) that may potentially affect the overall equipment performance and effectiveness. As such, it may be desirable to use condition monitoring techniques to monitor and diagnose the mechanical condition of the industrial systems.
Oftentimes, monitoring and diagnosing the mechanical condition of machine components may be complicated due to the complex nature of the machine components within the system. For example, power generation plants and oil and gas operations each involve a plurality of interrelated systems, with each system including complex and extensive machinery. Monitoring the condition of each machine component within the extensive system may involve a large amount of man power and/or time. Further, diagnosing a specific machine component failure from within a system of interrelated machine components may involve extensive knowledge, skill, or resources not readily available. Accordingly, various condition monitoring techniques may be used to monitor and diagnose machine components of industrial systems. For example, machine component failures may be monitored with a plurality of sensors disposed on the equipment, such that each sensor is configured to measure a machine condition parameter. However, it may be unwieldy and time consuming to analyze each parameter from the plurality of sensors in order to diagnose a machine component failure. Further, it may be cost-prohibitive to perform such monitoring. In certain situations, machine component failures may be monitored through vibration signal analysis, where vibration signals are indicative of a machine's mechanical condition. However, determining a machine component failure diagnosis from vibration signals often involves vibration specialists and/or specialized procedures and/or tasks. A vibration specialist visually and subjectively analyzes vibration signals, and an inaccurate and/or delayed analysis could be costly and/or time consuming. In addition, a vibration specialist may not always be readily available. Accordingly, improved systems and methods for monitoring and diagnosing the mechanical condition of equipment are desirable.